Valuation algorithms allow owners of content to associate economic or business value to their data assets. This value can then potentially be used as input to a variety of business functions.
By way of one example only, one business function includes insuring data assets against loss. Insuring valuable data against loss (e.g., breach or corruption) has become an important part of risk management for entities that store and/or manage data for clients. Since client data is typically stored in cloud computing platforms, and thus susceptible to online breach by identity thieves and other actors involved in illicit activities, insuring the heavy financial risk faced by an entity that maintains client data has become a necessity. The value placed on a data asset determines the cost of insuring the data. Of course, valuation of data assets of an entity can be useful in many other business functions.
In each case, typically, the value of the data is connected, at least in part, to the relevance of the data to the entity. However, determining data relevance can be a complex matter for an entity. Accordingly, it is realized that techniques for determining accurate relevance are important.